{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Getting Started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The top of Python files should always be a short documentation about the\n",
    "content of the file, or \"docstring\".\n",
    "\n",
    "This ipyton notebook is short demonstration of Python for scientific data analysis\n",
    "\n",
    "This script covers the following points:\n",
    "\n",
    "* Plotting a sine wave\n",
    "* Generating a column matrix of data\n",
    "* Writing data to a text-file, and reading data from a text-file\n",
    "* Waiting for a button-press to continue the program exectution\n",
    "* Using a dictionary, which is similar to MATLAB structures\n",
    "* Extracting data which fulfill a certain condition\n",
    "* Calculating the best-fit-line to noisy data\n",
    "* Formatting text-output\n",
    "* Waiting for a keyboard-press\n",
    "* Calculating confidence intervals for line-fits\n",
    "* Saving figures\n",
    "\n",
    "For such a short program, the definition of a \"main\" function, and calling\n",
    "it by default when the module is imported by the main program, is a bit\n",
    "superfluous. But it shows good Python coding style.\n",
    "\n",
    "Author: Thomas Haslwanter, Feb-2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Modules and Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# To see the plots inline, even if you have not started the notebook \n",
    "# via \"ipython notebook --pylab=inline\"\n",
    "% pylab inline\n",
    "\n",
    "# Note: single comment lines are indicated by \"#\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "In contrast to MATLAB, you explicitly have to load the modules that you need.\n",
    "And don't worry here about not knowing the right modules: *numpy*, *scipy*, and\n",
    "*matplotlib.pyplot* are almost everything you will need most of the time, and you\n",
    "will quickly get used to those.\n",
    "\n",
    "*pylab* automatically imports the most important components from numpy and\n",
    "matplotlib into the current workspace."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Sine Wave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Create a sine-wave\n",
    "t = arange(0,10,0.1)\n",
    "x = sin(t)\n",
    "\n",
    "# \"arange\" and \"sin\" are from the package \"numpy\". But since the command\n",
    "# \"pylab\" already loaded \"numpy\" into the current workspace, they are known\n",
    "# here.\n",
    "\n",
    "# Next, save the data in a text-file, in column form.\n",
    "# The formatting is a bit clumsy: data are by default row variables; so to\n",
    "# get a matrix, you stack the two rows above each other, and then transpose\n",
    "# the matrix.\n",
    "outFile = 'test.txt'\n",
    "savetxt(outFile, vstack([t,x]).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x238971d03c8>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2d/HxgTqKdO0Fr3euRKfVSa6jicHH3TQykdAgm/ZOcrGeRv1urUbyAT2dAyJY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\naGJQSil1AU0MSimlLvD/A7MO4+Q1g5xoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2389457e828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read the data into a different variable\n",
    "inData = loadtxt(outFile)\n",
    "t2 = inData[:,0] # Note that Python starts at \"0\"!\n",
    "x2 = inData[:,1]\n",
    "\n",
    "# Note: Python used (...) for function arguments, and [...] for indexing.\n",
    "\n",
    "# Plot the data\n",
    "plot(t,cos(t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Rotating a Vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "When working with vectors and matrices, keep the following things in mind\n",
    "* By default, data are vectors.\n",
    "* Use *array* when you want to generate matrices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Noisy Data and Linefits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Fit the following function: $y = k*x + d$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Generate a noisy line\n",
    "t = arange(-100,100)\n",
    "\n",
    "# use a Python \"dictionary\" for named variables\n",
    "par = {'offset':100, 'slope':0.5, 'noiseAmp':4}\n",
    "x = par['offset'] + par['slope']*t + par['noiseAmp']*randn(len(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2bdf9e1d160>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uyL7YOkSuzWgYlohZS29W3gw7vXSPeCjMyeIXx/twuIwvAHMz8aujFlzZrBbu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\ncBLohRAiw0mgF0KIDPf/AW44UHJJMHqqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2bdf9d96080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boolean indexing works in Python: select \"late\" values, i.e. with t>10\n",
    "xHigh = x[t>10]\n",
    "tHigh = t[t>10]\n",
    "\n",
    "# Plot the \"late\" data\n",
    "plot(tHigh, xHigh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Boolean indices can be combined:\n",
    "x = arange(10)\n",
    "topRange = x>2\n",
    "bottomRange = x<8\n",
    "x[topRange & bottomRange]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "   ## Fitting a line to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Determine the best-fit line\n",
    "# To do so, you have to generate a so-called Design Matrix, with \"time\" in the first\n",
    "# column, and a column of \"1\" in the second column:\n",
    "xMat = vstack((tHigh, ones(len(tHigh)))).T\n",
    "slope, intercept = linalg.lstsq(xMat, xHigh)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit line: intercept = 100.251, and slope = 0.488\n"
     ]
    },
    {
     "data": {
      "image/png": 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1YXYCj6w5QI2lmZiwoG493lcv+o6WThvF0mmj/H5eCNkZK0aMdQcqiA8Pcvdr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\nZcJVoS+llAlXhb6UUibcX6HQU+Nbu1jSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2bdf9e39198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show and plot the fit, and save it to a PNG-file with a medium resolution.\n",
    "# The \"modern\" way of Python-formatting is used\n",
    "plot(tHigh, xHigh)\n",
    "plot(tHigh, intercept + slope*tHigh, 'r')\n",
    "savefig('linefit.png', dpi=200)\n",
    "print('Fit line: intercept = {0:5.3f}, and slope = {1:5.3f}'.format(intercept, slope))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.885\n",
      "Model:                            OLS   Adj. R-squared:                  0.884\n",
      "Method:                 Least Squares   F-statistic:                     671.9\n",
      "Date:                Sat, 04 Feb 2017   Prob (F-statistic):           1.09e-42\n",
      "Time:                        14:27:52   Log-Likelihood:                -260.40\n",
      "No. Observations:                  89   AIC:                             524.8\n",
      "Df Residuals:                      87   BIC:                             529.8\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    100.2512      1.143     87.697      0.000      97.979     102.523\n",
      "x              0.4881      0.019     25.921      0.000       0.451       0.526\n",
      "==============================================================================\n",
      "Omnibus:                        0.760   Durbin-Watson:                   1.719\n",
      "Prob(Omnibus):                  0.684   Jarque-Bera (JB):                0.850\n",
      "Skew:                          -0.204   Prob(JB):                        0.654\n",
      "Kurtosis:                       2.750   Cond. No.                         143.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# If you want to know confidence intervals, best switch to *pandas*\n",
    "# Pandas is mainly used for statistics and worksheet-like data\n",
    "import pandas\n",
    "\n",
    "# The calculation of OLS has been moved to *statsmodels* now\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# Note that this is an advanced topic, and requires new data structures\n",
    "# such ad \"DataFrames\" and \"ordinary-least-squares\" or \"ols-models\".\n",
    "myDict = {'x':tHigh, 'y':xHigh}\n",
    "df = pandas.DataFrame(myDict)\n",
    "model = smf.ols('y~x', df).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# More Python Info on the Web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "[http://scipy-lectures.github.com/](http://scipy-lectures.github.com/) Python Scientifc Lecture Notes. **If you read nothing else, read this!**\n",
    "\n",
    "[http://wiki.scipy.org/NumPy_for_Matlab_Users/](http://wiki.scipy.org/NumPy_for_Matlab_Users/) Start here if you have lots of Matlab experience.\n",
    "\n",
    "[https://docs.python.org/3.6/tutorial/](https://docs.python.org/3.6/tutorial/) The Python tutorial. The original introduction.\n",
    "\n",
    "[http://jrjohansson.github.com/](http://jrjohansson.github.com/) Lectures on scienti\f",
    "c computing with Python. Great ipython notebooks!"
   ]
  }
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